GLM-4.7-Flash vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GLM-4.7-Flash by Zhipu AI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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GLM-4.7-Flash
Deep-Reasoning-ModellGLM-4.7-Flash von Zhipu AI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 200K Token zu API-Preisen von $0.015/1M Input- und $0.03/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.01 / 1M
$0.03 / 1M
200K tokens
N/A
Claude Opus 5.5
Deep-Reasoning-ModellClaude Opus 5.5 von Anthropic ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $4.0/1M Input- und $20.0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Wichtigste Unterschiede auf einen Blick
- GLM-4.7-Flash is 468.6x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 200K tokens).
- GLM-4.7-Flash is faster at 160 tok/s vs 78 tok/s.
| Feature | GLM-4.7-Flash | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 74 | 94 |
Provider | Zhipu AI | Anthropic |
Input Price Cost per million input tokens | $0.01 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.03 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.01 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 160 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 84.6% |
GPQA Diamond Graduate-level reasoning | N/A | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.40 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkEvaluations ongoing
Geprüfte Benchmark-Ergebnisse im Vergleich
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GLM-4.7-Flash · Strengths & Key Advantages
- Fortschrittliche Optimized High-Speed Lightweight Transformer-Architektur von Zhipu AI mit 200K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.015/1M Input, $0.03/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Claude Opus 5.5 · Strengths & Key Advantages
- Fortschrittliche Extended Autonomous Hybrid Reasoning Transformer-Architektur von Anthropic mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($4.0/1M Input, $20.0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-4.7-Flash | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $3.85 |
Output Tokens / Task Tokens generated per task | 12K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 42K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.7-Flash | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | glm-4-7-flash | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-01 | 2026-09 |
| Knowledge Cutoff | November 2025 | June 2026 |
| Architecture | Optimized High-Speed Lightweight Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.015/1M in) across full 200K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.002/1M tokens | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
GLM-4.7-Flash vs Claude Opus 5.5: Schnelle Antworten (FAQ)
Which is more intelligent, GLM-4.7-Flash or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 74 on the composite Intelligence Index.
Which model is faster?
GLM-4.7-Flash is faster, generating 160 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
GLM-4.7-Flash is cheaper at $0.01 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GLM-4.7-Flash has lower latency, with a time to first token of 0.24s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 200K tokens.
Fazit
Choose GLM-4.7-Flash for unternehmensautomatisierung, software-engineering, autonome ki-agenten und zhipu ai-produktionssysteme, or choose Claude Opus 5.5 for unternehmensautomatisierung, software-engineering, autonome ki-agenten und anthropic-produktionssysteme.
Häufig gestellte Fragen (FAQ)
Which is cheaper: GLM-4.7-Flash or Claude Opus 5.5?+
GLM-4.7-Flash costs $0.015/1M input and $0.03/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: GLM-4.7-Flash or Claude Opus 5.5?+
GLM-4.7-Flash supports 200K tokens whereas Claude Opus 5.5 supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Offizielle Quellen: Zhipu AI & Anthropic Official Documentation